The proliferation of smartphones has completely changed the traffic characteristics of cellular networks. With multiple applications (apps) on each device often simultaneously downloading email, music, videos, web content, etc., the data load on carriers is heavy and can adversely affect both carriers and users. Additionally, research has shown that almost thirty percent of smartphones have six or more apps running in the background all day. Without incentives to minimize their data loads, many apps have gotten careless in their use of finite network resources.
Advances in modern generation cellular technologies, to include without limitation Long Term Evolution (LTE) and other fourth generation (4G) technologies, have increased overall network capacity, but network resources remain finite and effective data management is crucial. Conventional data management approaches include quality of service (QoS) methods wherein transferred data packets are classified and may be allocated predefined bit rates and minimal latency constraints. Other management techniques include scheduling, bandwidth control, and power manipulation. However, these prior art methods do not always differentiate between various types and purposes of data and can be inefficient and not optimal.
Therefore a need exists for improved management of cellular data traffic.